{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"6e9d10aa-49d2-4e23-ba39-8a7c0475817b","company_id":"43dd17db-bec3-4ebb-a432-f71d57a9aa47","title":"Senior Data Scientist, CompBio","slug":"senior-data-scientist-compbio-d84439dd","description":"THE OPPORTUNITY\n\nState-of-the-art technologies that measure multiple cellular aspects of in vitro biology are at the heart of insitro's efforts to accelerate drug development. Computational biology is key to elucidating the relationship between these phenotypes and human disease and translating them into actionable outcomes.\n\nWe are looking for a computational biologist with expertise across diverse data modalities, including deep experience in either omics or imaging readouts, a strong understanding of cell and disease biology, and fluency with state-of-the-art analysis techniques. Your expertise will help the team navigate the complexities of identifying therapeutic targets from diverse data, elucidating biological mechanisms, and championing a culture of statistical rigor and experimental design to ensure our analyses meet the highest scientific standards.\n\nIn this role, you will directly impact target prioritization and drug development efforts, advance our understanding of diseases, and aid the development of new treatments. You will be part of a cross-functional team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists who strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity. Based in South San Francisco, this position reports directly to the Head of Computational Biology and ML-Omics and offers an in-person hybrid schedule of three days per week.\n\nYou will be joining a vibrant biotech startup with many opportunities for significant impact. You will work closely with a highly talented team, learn a broad range of skills, and help shape insitro's culture, strategic direction, and outcomes. Join us, and help make a difference to patients!\n\n \n\n\nRESPONSIBILITIES\n\nMultimodal Analysis \u0026 Target Discovery\n\n - Synthesize Multimodal Insights: Draw insights from multimodal analyses (microscopy, spatial proteomics, bulk/single-cell RNA-seq, human cohort data) to uncover disease mechanisms and generate therapeutic hypotheses\n\n - Identify Therapeutic Targets: Analyze diverse data from disease-relevant in vitro models to identify potential therapeutic targets from perturbation screens\n\n - Discover Biomarkers: Analyze data from diverse sources to identify and validate potential biomarkers for monitoring disease status and progression\n\nExperimental Partnership\n\n - Partner on Experimental Design: Work with experimental biologists to design, troubleshoot, and optimize experiments that generate and validate mechanistic and therapeutic hypotheses\n\n - Provide Domain Expertise: Bring statistical and computational expertise to guide assay development and the biological interpretation of results\n\nAnalytical Rigor \u0026 Communication\n\n - Benchmark and Calibrate: Calibrate analysis tools and workflows, define performance metrics, and conduct benchmarking to select fit-for-purpose solutions\n\n - Communicate Findings: Share results with cross-functional stakeholders through reports, visualizations, presentations, and publications\n\n \n\n\nABOUT YOU\n\nExperience \u0026 Qualifications\n\n - Education \u0026 Tenure: Ph.D. in computational biology, systems biology, bioengineering, computer science, machine learning, or a related discipline, with 3+ years of working experience post-graduation\n\n - Data Modality Depth: Hands-on experience with diverse data modalities, including at least one of the following: single-cell RNA-seq, fluorescence microscopy, spatial proteomics or transcriptomics, or label-free microscopy\n\n - Statistical Foundation: Deep understanding of statistical modeling and data analysis, with a demonstrated ability to rigorously interpret complex datasets and generate mechanistic hypotheses\n\n - Biological Grounding: An understanding of molecular biology or disease biology (e.g., neurological, cardiovascular, or metabolic disorders)\n\n - Programming Skills: Strong programming ability and proficiency with Python scientific packages such as NumPy and pandas\n\n - Publication Record: Meaningful contributions to high-quality work published in relevant computational biology, systems biology, life sciences, or biomedical venues\n\nCore Competencies\n\n - Collaborative Communicator: You communicate effectively and collaborate well with people of diverse backgrounds and job functions\n\n - Engineering Discipline: You write well-commented code and documentation and are familiar with coding best practices such as version control and code review\n\n \n\n\nCOMPENSATION \u0026 BENEFITS AT INSITRO\n\nOur target starting salary for successful US-based applicants for this role is $183,000 - $194,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.\n\nThis role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) an","salary_min":183000,"salary_max":194000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","payments","machine-learning","data-science"],"apply_url":"https://jobs.ashbyhq.com/insitro/ec4a278a-dd78-4e9c-a203-87e3e2f59e60/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T21:54:47.039Z","expires_at":"2026-09-29T13:36:50.193262Z","created_at":"2026-08-29T13:37:15.708222Z","updated_at":"2026-08-30T13:36:50.329078Z","company_name":"Insitro","company_slug":"insitro","company_logo_url":"https://www.google.com/s2/favicons?domain=insitro.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6e9d10aa-49d2-4e23-ba39-8a7c0475817b"},{"id":"a9304478-6b78-4ca2-a86c-1622c0c977f2","company_id":"654d4532-88db-435d-8a6f-161b8c5a491e","title":"Senior Manager, Data Science - Styling Algorithms","slug":"senior-manager-data-science-styling-algorithms-ed2bd7cb","description":"About Stitch Fix, Inc. \n Stitch Fix (NASDAQ: SFIX) Stitch Fix is redefining retail by combining human creativity with advanced data science and Generative AI. As we build the future of personalized shopping, we’re equally committed to building yours.  We believe in investing in our team as much as our technology. Join us to be a trendsetter in the industry and help us redefine what’s possible for our clients, while we help you reach your full potential.\n About the Role \n At Stitch Fix, we are at the forefront of innovation, creating cutting-edge solutions that blend fashion, technology, and data science. Our data science team combines machine learning with expert human judgment to generate innovative recommendations and insights that transform the way our clients discover what they love. We believe in a curiosity-driven data science culture where members are empowered to deliver impact through end-to-end model development. The diversity of the problems that we work on and the data-rich environment of our business make it possible, even essential, to bring the tools of multiple disciplines to bear on our hardest problems.  \n We are looking for an experienced Styling Algorithms Team Manager to lead a group of talented machine learning engineers and data scientists. In this role, you will shape the future of fashion technology by driving the development and deployment of our styling algorithms, which empower our human stylists to delight clients by nailing their fit and style. This includes ML-, AI-, and product-driven feature curation and testing for our proprietary styling platform, as well as client-facing AI personalization experiences, such as Stitch Fix Vision, our virtual try-on.\n Responsibilities: \n \n Champion bold AI and ML interventions to improve our styling experiences, enabling our stylists to have a multiplicative impact on their client connection points.\n Likewise, actively shape the product roadmap for direct client-facing styling experiences, expanding the breadth and depth of personalization touchpoints to complement and inform our human stylists.\n Inspire your team by fostering a culture of ideation, ownership, feedback, and collaboration between team members and with cross-functional partners.\n Act as an advocate for our Styling and Merchandising teams, empowering partners to understand trends in stylist feedback and inventory surfacing algorithms for rapid action on emergent opportunities. \n Work with product managers, other data science teams, UI/UX designers, and business leaders to define and optimize against business objectives for our suite of styling experiences.\n Oversee the end-to-end algorithm development lifecycle, from ideation and experimentation to testing and deployment in a production environment.\n Identify and implement best practices for team collaboration, code quality, use of AI, and data management.\n Stay up-to-date with advancements in AI-assisted development, AI-enabled product experiences, machine learning, and fashion technology.\n \n About You \n This is what you’ll need to succeed in this role from day 1. \n Requirements: \n \n Bachelor’s Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field required. Master’s or PhD preferred. \n 5+ years of experience in design and deployment of AI and ML solutions, ideally in retail personalization, with an emphasis on agentic capabilities.\n 2+ years of experience as a team technical lead or direct people manager.\n Ability to write and review production-grade code, ideally in Python.\n Applied knowledge of AI-assisted coding best practices and development of agentic product solutions.\n Excels at building trust with your team, stakeholders, and technical partners.\n Excellent communication skills with the ability to articulate complex technical concepts to business audiences.\n Experience with online A/B testing, experimentation frameworks, and performance metrics.\n Familiar with cloud-based infrastructure and distributed data systems.\n Compensation and Benefits This role will receive a competitive salary, benefits, and equity. The salary for US-based employees hired into this role will be aligned with the range below, which includes our three geographic areas. A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, location, and performance. This position is eligible for an annual bonus, and new hire and ongoing grants of restricted stock units, depending on employee and company performance. In addition, the position is eligible for medical, dental, vision, and other benefits. Applicants should apply via our internal or external careers site. \n Salary Range\n $200,000 — $246,000 USD \n This link leads to the machine readable files that are made available in response to the federal Transparency in Coverage Rule and includes negotiated service rates and out-of-network allowed amounts","salary_min":200000,"salary_max":246000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["generative-ai","payments","healthcare","agents","data-science"],"apply_url":"https://www.stitchfix.com/careers/jobs?gh_jid=8164607\u0026gh_jid=8164607","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T16:49:32Z","expires_at":"2026-09-29T13:49:16.042671Z","created_at":"2026-08-29T13:50:46.465007Z","updated_at":"2026-08-30T13:49:16.172549Z","company_name":"Stitch Fix","company_slug":"stitch-fix","company_logo_url":"https://www.google.com/s2/favicons?domain=stitchfix.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a9304478-6b78-4ca2-a86c-1622c0c977f2"},{"id":"0da6da6c-77b3-4ba4-97ca-4eb47129b83d","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Data Scientist, Ads Integrity","slug":"senior-data-scientist-ads-integrity-254eca52","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit is continuing to grow our teams with the best talent. This role is completely remote friendly within the United States. If you happen to live close to one of our physical office locations (San Francisco, Los Angeles, New York City \u0026 Chicago) our doors are open for you to come into the office as often as you'd like.\n Reddit is poised to innovate and grow like never before, and Safety is a critical accelerant of that growth. The Safety org is Reddit’s central Trust \u0026 Safety organization, protecting users from bad experiences by stopping harmful content, behaviors, and abuse across the platform. We are looking for a Senior Data Scientist to lead ads fraud detection and scaled enforcement within Safety. You will partner closely with Ads Product, Engineering, Machine Learning, Operations, Policy, Legal, and fellow Safety data scientists to identify emerging ads fraud, define rigorous measurement and evaluation standards, and turn investigations into durable signals, models, rules, and enforcement pipelines. This is a high-impact role with exceptional opportunity for ownership and growth: as an early leader in a greenfield space, you will help define the strategy, shape cross-functional roadmaps, build foundational capabilities, and expand your scope as Reddit’s ads integrity program matures.\n Responsibilities\n \n Lead the measurement and detection strategy for ads fraud by defining fraud taxonomies, labels, sampling plans, metrics, and evaluation frameworks that make performance measurable and defensible.\n Analyze large, complex datasets and networks of behavior to uncover emerging fraud patterns, size their impact, identify root causes, and translate findings into detection and enforcement requirements.\n Design and develop scalable ads fraud detection and enforcement pipelines in partnership with Engineering and Machine Learning, including feature generation, rules and models, near-real-time scoring, actioning, review feedback loops, and observability.\n Own the full detection lifecycle: backtesting, threshold calibration, offline and online evaluation, launch validation, experimentation, monitoring, drift detection, incident response, rollback, and retirement.\n Build and maintain statistical, machine learning, and GenAI-enabled models or prototypes that improve fraud detection, risk identification, investigator efficiency, and enforcement quality.\n Balance fraud loss, platform and advertiser risk, customer experience, false-positive costs, operational capacity, and business goals when recommending detection thresholds and enforcement strategies.\n Partner across Ads and Safety to shape strategy and roadmaps, strengthen data foundations, close policy and enforcement gaps, and ensure solutions meet governance and compliance standards.\n Translate complex analyses into clear narratives and actionable recommendations for technical and non-technical stakeholders, including senior leaders, and mentor other data scientists and analysts.\n \n Qualifications\n \n Relevant experience in Data Science, Applied Science, or a related quantitative role, preferably in ads fraud, financial fraud, or account risk, Trust \u0026 Safety, platform integrity, or enforcement engineering.\n Ph.D. or M.S. degree in Statistics, Economics, Computer Science, Applied Mathematics, or another quantitative field; with an M.S., 4+ years of industry data science experience, or with a Ph.D., 2+ years of industry data science experience.\n Demonstrated experience building or materially shaping production detection and automated enforcement pipelines, including batch or streaming data, feature engineering, rules or models, decisioning, monitoring, and feedback loops.\n Strong command of fraud or abuse detection methods and evaluation, including label design, precision and recall tradeoffs, calibration, threshold selection, false-positive analysis, drift detection, and adversarial adaptation.\n Experience partnering closely with Product and Engineering teams to translate analyses and prototypes into reliable production systems; experience working across Ads, Safety, fraud, risk, or platform-integrity organizations is preferred.\n Experience applying AI and large language models (LLMs) to practical data science workflows, such as threat discovery, content classification, signal development, investigation automation, or detection and enforcement systems.\n Deep understanding of complex behavioral networks or large-scale activity patterns; experience with methods such as graph or network analysis, clustering","salary_min":190800,"salary_max":267100,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["generative-ai","nlp","healthcare","llm","data-science"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8157580","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T20:49:14Z","expires_at":"2026-09-29T13:38:57.712667Z","created_at":"2026-08-29T13:39:34.881738Z","updated_at":"2026-08-30T13:38:57.847667Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0da6da6c-77b3-4ba4-97ca-4eb47129b83d"},{"id":"e17eda01-e317-4c5f-9dae-d8e3404b1b2e","company_id":"ec4a8bb4-3840-4054-8ccd-77e81db037af","title":"Data Scientist/Senior Data Scientist","slug":"data-scientistsenior-data-scientist-565352fb","description":"C3 AI (NYSE: AI), is the Enterprise AI application software company. C3 AI delivers a family of fully integrated products including the C3 Agentic AI Platform, an end-to-end platform for developing, deploying, and operating enterprise AI applications, C3 AI applications, a portfolio of industry-specific SaaS enterprise AI applications that enable the digital transformation of organizations globally, and C3 Generative AI, a suite of domain-specific generative AI offerings for the enterprise. Learn more at: C3 AI \n As a member of the C3 AI Data Science team , you will work with some of the largest companies on the planet helping them build the next generation of AI-powered enterprise applications on the C3 AI Platform. You will work directly with data scientists, AI engineers, and subject matter experts to design and deploy AI capabilities that give our customers the information they need to make better decisions and accelerate their digital transformation. You will identify the right AI approaches for each problem and implement them on the C3 AI Platform so they run reliably at enterprise scale.\n Qualified candidates will have deep knowledge of modern AI and ML techniques — including large language models, agentic systems, and classical statistical methods — along with a clear understanding of their limitations and how to adapt them to large-scale production environments. Some travel is expected.\n Note: This is a client-facing position which requires travel. Candidates should have the ability and willingness to travel based on business needs. \n Responsibilities: \n \n Lead the research, design, implementation, and deployment of AI models, agentic solutions, and optimization algorithms for enterprise applications on the C3 AI Platform.\n Partner with C3 AI customers to build and scale their own AI applications on the Platform.\n Contribute to the design and implementation of new AI capabilities within the C3 AI Platform.\n Analyze model performance across enterprise deployments, diagnose issues such as poor recall or false positive rates, and recommend targeted improvements.\n Collaborate with data engineers and subject matter experts from C3 AI and customer teams to source, validate, and correctly leverage new data assets.\n \n Qualifications: \n \n MS or PhD in Computer Science, Electrical Engineering, Statistics,   Operations Research, or a related field.\n Hands-on AI experience spanning generative AI, agentic systems, supervised and unsupervised learning, and classical regression and classification.\n Strong mathematical foundation in linear algebra, calculus, probability, and statistics.\n Experience building and deploying models at scale in distributed or cloud-native environments.\n Ability to drive projects independently and collaborate effectively across technical and non-technical teams.\n Sharp, motivated, and focused on making a real impact.\n Excellent verbal and written communication skills.\n \n Preferred Qualifications: \n \n Proficiency in Python; experience with JavaScript, Java, or Scala is a plus.\n Familiarity with LLM frameworks (e.g., LangChain, LlamaIndex), vector databases, or RAG architectures.\n A portfolio of AI projects (GitHub, publications, or open-source contributions) is a plus.\n C3 AI provides excellent benefits, a competitive compensation package and generous equity plan. \n California Base Pay Range\n $136,000 — $183,000 USD \n C3 AI is proud to be an Equal Opportunity and Affirmative Action Employer. We do not discriminate on the basis of any legally protected characteristics, including disabled and veteran status.","salary_min":136000,"salary_max":183000,"location":"Redwood City, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","llm","rag","embeddings","agents","data-science"],"apply_url":"https://c3.ai/job-description/8751111002?gh_jid=8751111002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T17:07:29Z","expires_at":"2026-09-29T13:40:09.638586Z","created_at":"2026-08-27T13:40:52.550205Z","updated_at":"2026-08-30T13:40:09.780051Z","company_name":"C3 AI","company_slug":"c3-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=c3.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e17eda01-e317-4c5f-9dae-d8e3404b1b2e"},{"id":"d4653f80-c2d2-4f90-b750-70a300947a71","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Manager of Data Science Production Engineering, Data Engineering \u0026 Delivery","slug":"manager-of-data-science-production-engineering-data-engineering-delivery-a1a2ddc4","description":"This is an exciting opportunity to lead a Data Engineering \u0026 Delivery (DED) team at Natera, a global leader in precision medicine and genomics testing.  As a Manager of the DED team inside our Data Science Production Engineering (DSPE) department, you will have the opportunity to work with Natera's diverse and ultra-large data sets (up to PB size), develop innovative solutions with the latest information and cloud technologies, and make real impact by providing timely, accurate, and robust data products and data delivery/automation systems to support Natera's Lab Operations and Genetic Counselor/Lab Director teams, and ultimately impact patients' medical outcomes.\n PRIMARY RESPONSIBILITIES:  \n Leadership \n \n Lead a Data Engineering \u0026 Delivery (DED) team, collectively develop and maintain the data infrastructure, data ingestion solutions, ETL pipelines, robust data products, and data delivery solutions, for both production support and DSPE internal applications.\n Manage the Data Engineering \u0026 Delivery (DED) team, to achieve the timely and efficient delivery of robust data products and systems that can meet DSPE business needs and project requirements.\n Measure, report, and maintain/improve operational effectiveness.\n Train and coach team members on new technologies, procedures, and guidelines/best practices.\n \n Technical \n \n Lead the Data Engineering \u0026 Delivery (DED) team, collectively develop and maintain the data infrastructure, data ingestion solutions, ETL pipelines, robust data products, and data delivery solutions, for both production support and DSPE internal applications\n Translate DSPE's business needs and project requirements into technical specifications and implementation plans.\n Working with DSPE leadership teams, create design documents that can be efficiently implemented and maintained, for data system architecture, data ETL pipeline, data delivery solution, and other data systems/tools/components.\n Contribute to key development, testing, deployment, validation, maintenance, and update activities on data products and data systems/tools/components, as well as data delivery tasks.\n \n Quality, Compliance, and Documentation \n \n Improve and enforce operating procedures for compliance, quality, and efficiency.\n Create training materials, and contribute to training activities, on DSPE Data Engineering \u0026 Delivery (DED) team owned data products and data systems.\n \n Cross-Functional \n \n Engage proactively and directly with internal and external stakeholders to align development roadmaps, resolve dependencies, and exchange performance metrics.\n Represent the Data Engineering \u0026 Delivery (DED) team in stakeholder meetings.\n \n QUALIFICATIONS: \n \n Masters degree in Statistics, Data Science, Bioinformatics, Computer Science, Mathematics, Management Science, Operational Research, or other related fields. Doctorate degrees are a plus.\n Minimal 5 years of relevant industry experience for candidates with a Master's Degree.  Minimal 2 years of relevant industry experience for candidates with a Doctorate degree.\n Minimal 2 years of working experience in a highly regulated environment, e.g., CLIA and/or FDA compliant settings.\n Minimal 2 years of management experience leading a Data Engineering team, a Data Production/Delivery team, or a Data Analytics team, with demonstrable evidence of leadership and team management skills.\n \n KNOWLEDGE, SKILLS, AND ABILITIES: \n \n Strong proficiency in SQL and Python programming languages. Proficiency in R is a plus.\n Proficiency in data analysis and statistical techniques.\n Proficiency in data visualization tools (e.g., Tableau, Power BI, AWS QuickSight).\n Strong SQL skills in developing robust and maintainable queries to extract and/or aggregate data from complex data sources.\n Attention to detail and a commitment to data accuracy.\n Proficiency in building data products, e.g., data aggregation, extraction, transformation, and QC. Past working experience in dbt is a plus.\n Excellent problem-solving and critical-thinking abilities.\n Working knowledge of genomics and bioinformatics workflows. Human genetics knowledge is a plus.\n Proficiency in Git workflow and version control systems like GitHub and/or GitLab.\n Working experience with healthcare data, electronic health records (EHR), and clinical terminology is a plus.\n Ability to work collaboratively in a cross-functional team environment.\n Good documentation practices and coding style.\n Strong verbal and written communication skills.\n Strong interpersonal skills.\n Strong integrity.\n \n Compensation \u0026 Total Rewards  \n This range reflects a good-faith estimate of the base pay we reasonably expect to offer at the time of  hire. Final compensation will vary based on experience, qualifications, and internal equity considerations. \n This position is also eligible for additional compensation and benefits through Natera’s robust Total Rewards program, including: \n \n \n Annual performance incentive bonus \n \n Long-term equity awar","salary_min":139900,"salary_max":174900,"location":"San Carlos, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","data-pipeline","healthcare","data-science","data-engineering"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6150570004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-21T20:13:10Z","expires_at":"2026-09-29T13:40:54.756368Z","created_at":"2026-08-25T18:29:44.00758Z","updated_at":"2026-08-30T13:40:54.89509Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d4653f80-c2d2-4f90-b750-70a300947a71"},{"id":"2def9ded-6b75-4209-9cee-cda6c10334c1","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Manager of Data Science Production Engineering, Data Engineering \u0026 Delivery","slug":"manager-of-data-science-production-engineering-data-engineering-delivery-a8bbb51b","description":"This is an exciting opportunity to lead a Data Engineering \u0026 Delivery (DED) team at Natera, a global leader in precision medicine and genomics testing.  As a Manager of the DED team inside our Data Science Production Engineering (DSPE) department, you will have the opportunity to work with Natera's diverse and ultra-large data sets (up to PB size), develop innovative solutions with the latest information and cloud technologies, and make real impact by providing timely, accurate, and robust data products and data delivery/automation systems to support Natera's Lab Operations and Genetic Counselor/Lab Director teams, and ultimately impact patients' medical outcomes.\n PRIMARY RESPONSIBILITIES:  \n Leadership \n \n Lead a Data Engineering \u0026 Delivery (DED) team, collectively develop and maintain the data infrastructure, data ingestion solutions, ETL pipelines, robust data products, and data delivery solutions, for both production support and DSPE internal applications.\n Manage the Data Engineering \u0026 Delivery (DED) team, to achieve the timely and efficient delivery of robust data products and systems that can meet DSPE business needs and project requirements.\n Measure, report, and maintain/improve operational effectiveness.\n Train and coach team members on new technologies, procedures, and guidelines/best practices.\n \n Technical \n \n Lead the Data Engineering \u0026 Delivery (DED) team, collectively develop and maintain the data infrastructure, data ingestion solutions, ETL pipelines, robust data products, and data delivery solutions, for both production support and DSPE internal applications\n Translate DSPE's business needs and project requirements into technical specifications and implementation plans.\n Working with DSPE leadership teams, create design documents that can be efficiently implemented and maintained, for data system architecture, data ETL pipeline, data delivery solution, and other data systems/tools/components.\n Contribute to key development, testing, deployment, validation, maintenance, and update activities on data products and data systems/tools/components, as well as data delivery tasks.\n \n Quality, Compliance, and Documentation \n \n Improve and enforce operating procedures for compliance, quality, and efficiency.\n Create training materials, and contribute to training activities, on DSPE Data Engineering \u0026 Delivery (DED) team owned data products and data systems.\n \n Cross-Functional \n \n Engage proactively and directly with internal and external stakeholders to align development roadmaps, resolve dependencies, and exchange performance metrics.\n Represent the Data Engineering \u0026 Delivery (DED) team in stakeholder meetings.\n \n QUALIFICATIONS: \n \n Masters degree in Statistics, Data Science, Bioinformatics, Computer Science, Mathematics, Management Science, Operational Research, or other related fields. Doctorate degrees are a plus.\n Minimal 5 years of relevant industry experience for candidates with a Master's Degree.  Minimal 2 years of relevant industry experience for candidates with a Doctorate degree.\n Minimal 2 years of working experience in a highly regulated environment, e.g., CLIA and/or FDA compliant settings.\n Minimal 2 years of management experience leading a Data Engineering team, a Data Production/Delivery team, or a Data Analytics team, with demonstrable evidence of leadership and team management skills.\n \n KNOWLEDGE, SKILLS, AND ABILITIES: \n \n Strong proficiency in SQL and Python programming languages. Proficiency in R is a plus.\n Proficiency in data analysis and statistical techniques.\n Proficiency in data visualization tools (e.g., Tableau, Power BI, AWS QuickSight).\n Strong SQL skills in developing robust and maintainable queries to extract and/or aggregate data from complex data sources.\n Attention to detail and a commitment to data accuracy.\n Proficiency in building data products, e.g., data aggregation, extraction, transformation, and QC. Past working experience in dbt is a plus.\n Excellent problem-solving and critical-thinking abilities.\n Working knowledge of genomics and bioinformatics workflows. Human genetics knowledge is a plus.\n Proficiency in Git workflow and version control systems like GitHub and/or GitLab.\n Working experience with healthcare data, electronic health records (EHR), and clinical terminology is a plus.\n Ability to work collaboratively in a cross-functional team environment.\n Good documentation practices and coding style.\n Strong verbal and written communication skills.\n Strong interpersonal skills.\n Strong integrity.\n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $127,200 — $159,000 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, w","salary_min":127200,"salary_max":159000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["cloud","healthcare","data-pipeline","data-engineering","data-science"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6137669004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-21T20:13:08Z","expires_at":"2026-09-29T13:40:54.857114Z","created_at":"2026-08-25T18:29:44.00289Z","updated_at":"2026-08-30T13:40:54.99466Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2def9ded-6b75-4209-9cee-cda6c10334c1"},{"id":"2af68795-5861-40b1-95ce-04d100978f08","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Senior Data Scientist","slug":"senior-data-scientist-e0977b92","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The Role\n As a Data Scientist supporting AI engineers, you will partner with one or more engineering teams, developing actionable insights that guide improvements to the Wayve AI Driver. Using experimental and observational analyses of real and simulated driving, you will help teams advance the functionality, safety, and performance of the Wayve AI Driver, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.\n This means you might:\n \n Formulate and iterate upon the performance metrics that organize our engineering efforts and guide progress toward commercial success\n Design experiments and targeted off-road measurements to ensure that we deliver product requirements to customers while maintaining safety and performance\n Investigate factors in model training and inference leading to bottlenecks in functionality and performance, identifying and validating hypotheses for unlocking improvements\n \n About you \n Essential:\n \n 3+ years experience working in a Data Science role.\n Fluent in querying and building large datasets, writing production-level SQL for use in data-transformation pipelines.\n Prior experience designing robust real-world experiments (e.g. A/B) and critically evaluating test-statistics\n Foundations in the fundamentals behind statistics: testing appropriate distributions, testing the assumptions behind frequentist stats\n Proficient in using a statistical scripting language and data science/ML packages (e.g. python such as pandas, sklearn, statsmodels, scipy or R such as dplyr, caret, stats)\n Well-versed in summarising, visualising and communicating findings in an accessible and compelling way\n Track record of influencing team direction through your findings\n A bias towards deriving actionable insight that can be used to drive prioritisation and strategy for others.\n Comfortable working asynchronously across time zones with cross-functional partners\n You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.\n \n Desirable:\n \n Practical experience with machine learning (e.g. PyTorch). Passion to take research ideas to production.\n Track record of promoting statistical rigour and experimental best practices in your prior roles.\n Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.\n Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)\n Experience working in a fast-moving tech company or startup.\n \n This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $209,700 to 266,800, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.   We operate core working hours so you can determine the schedule that works best for you and your team. \n Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran ","salary_min":209700,"salary_max":266800,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","distributed-systems","pytorch","generative-ai","data-science","evaluation"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8728411002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T20:06:09Z","expires_at":"2026-09-29T13:43:27.258396Z","created_at":"2026-08-25T18:31:14.400233Z","updated_at":"2026-08-30T13:43:27.392293Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2af68795-5861-40b1-95ce-04d100978f08"},{"id":"6753c0ee-7501-493c-89c5-8fdeebce90f8","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Data Scientist - Payments (Inference)","slug":"senior-data-scientist-payments-inference-a5421a89","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n You will join the Payments Data Science organization, which sits at the intersection of Trust and Payments and powers the systems that move money safely and efficiently across Airbnb's global marketplace. The team spans payment optimization for guests and hosts, fraud and risk mitigation, complex measurement, and regulatory compliance. We partner directly with Payments product and engineering leadership, Finance, and Trust to ensure every transaction is fast, safe, and compliant at global scale. Our work directly shapes decisions made by senior leaders, including Payments executive leadership, and requires a rigorous, evidence-based approach to every recommendation we make. Our Data Science team enables this mission by providing reliable measurement frameworks to deliver robust data insights, build and enable state-of-the-art data products/models, and provide actionable and reliable business guidance.\n The Difference You Will Make: \n We are looking for a passionate data scientist to lead quantitative measurement efforts and bring novel scientific approaches to drive decision making across our platform’s payment experience. This data scientist will perform careful hypothesis generation, causal inference framework development, and model development/evaluation to ideate and drive payment strategies on our platform. This role will have a particular focus on payments fraud mitigation and loss optimization, with the goal of making our platform safer for our community. Our Data Scientists have a deep understanding of causal framework development, statistical analysis, machine learning model development and evaluation strategies, and the complications of running experiment/quasi-experimental methods in a two-sided marketplace. They have keen business sense and are able to develop novel solutions to fraud and risk problems that don't have an established playbook and utilize their findings to communicate across a wide range of partners to drive our data \u0026 product roadmaps. They are not only the trusted data expert on their team, but also a storyteller.\n Examples of projects you may work on include, development of novel metrics and frameworks that can efficiently measure outcomes (often balancing competing tradeoffs), generating deep root cause investigations and long term impact measurements, and building/evaluating ML and agentic models to optimize guest, host, and business outcomes.\n A Typical Day:  \n \n Inference: Develop and apply causal inference methods, including experimental, econometric regressions, and quasi-experimental methods to measure a wide-range of platform/product impacts.\n AI/ML: Build methods for robust evaluation of ML/AI model efficiency and performance. Ability to identify use-cases for and develop predictive models to classify, segment, and interpret our users’ behavior. Support evaluation and optimization of agentic and LLM-based systems. \n Optimization:  Develop methodologies to explore/simulate the impact of new interventions and develop data products to optimize product/operational strategies.\n Communication:  Deliver robust research reports and effective data visualizations. Collaborate with and present to stakeholders to identify opportunities and communicate findings, and drive impact.\n Empowerment:  Think strategically about opportunities to improve and scale our brand measurement and customer insights.\n \n Your Expertise: \n \n 5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (math / economics / statistics, and etc.), or 3+ years of experience with a Phd degree.\n Strong knowledge of causal inference, experimentation, applied statistical modeling, and end-to-end ML development.\n Skilled in statistical programming (Python or R) and database usage (SQL)\n Demonstrated track record of owning a business or technical domain end-to-end at a prior company: setting your own roadmap, being the accountable expert others escalate to, and driving a problem to resolution.\n Proven ability to communicate clearly and effectively to audiences of varying technical levels\n Ability to work independently, set your own roadmap, and drive cross-functional alignment\n Payments Fraud/Risk Domain expertise is a strong plus.\n Familiarity with evaluating agentic or LLM-based systems (e.g., decision-quality measurement, human-in-the-loop calibration) is a plus.\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote ","salary_min":179000,"salary_max":210000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["llm","agents","payments","data-science","inference"],"apply_url":"https://careers.airbnb.com/positions/8123037?gh_jid=8123037","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-14T17:25:31Z","expires_at":"2026-09-29T13:39:39.386068Z","created_at":"2026-08-25T18:29:15.498252Z","updated_at":"2026-08-30T13:39:39.528611Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6753c0ee-7501-493c-89c5-8fdeebce90f8"},{"id":"bb4543bd-f8e6-4aa5-a3d1-44f7be51dd41","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Data Scientist","slug":"data-scientist-787a09f1","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n The Way We Work\n The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:\n \n Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.\n Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.\n Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.\n Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.\n Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.\n Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.\n \n  \n About the Role\n We are looking for a Data Scientist to drive informed decision-making across the company as we become the first fully-integrated neocloud for AI training. This individual will work closely with Product, Engineering, and Sales leadership, answering key questions about product usage, proactively surfacing trends, and supporting our growth in a data-driven way.\n You will join the Product Team and report to our VP of Product. \n This is a hybrid role based in New York City, NY with in-office requirements of 2 days per week. \n What You’ll Do\n \n Partner with Product, Sales, and Engineering to define questions that matter and identify product features and behaviors that drive business outcomes\n Analyze small business and enterprise usage patterns to sharpen Ideal Customer Profiles and guide Sales prospecting priorities\n Quantify the health of our data center business and test the impact of systematic sales, service, and operations improvements\n Work with engineering to design, build, and maintain data pipelines that support timely and accurate analyses\n Write SQL (and occasional scripts) to explore data, automate recurring workflows, and answer ad-hoc questions\n Design Looker and Data Studio dashboards and reports that make findings easy for stakeholders to act on\n Monitor trends and surface opportunities or risks early, before they show up in routine reporting\n Help grow a data-driven culture by sharing practices and enabling teams to use analytics tools on their own\n \n What You’ll Need\n \n 5+ years in a Data Science or Product Analyst role\n Proficient using SQL to query large databases\n Strong skills using Python to write scripts and run statistical analysis\n Hands-on experience with BI tools (Looker or similar) and cloud data infrastructure — AWS (e.g. S3, EFS) and/or GCP (e.g. BigQuery, Looker Studio)\n A strong bias for action, and the ability to make clear decisions and recommendations in the presence of uncertainty\n Experience on a growing data team at a startup, preferred\n Experience designing and building foundational data models from scratch, preferred\n We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits.\n The anticipated annual base salary range for this role is:\n $160,000 — $245,000 USD \n Benefits and Perks \n We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success:\n \n Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents.\n Meaningful Equity: RSUs that give employees a stake in the company's long-term success.\n Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.).\n Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance.\n Company-Wide Winter Break: Two ","salary_min":160000,"salary_max":245000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","pytorch","cloud","data-science"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7860776003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-13T00:11:00Z","expires_at":"2026-09-29T13:33:56.499785Z","created_at":"2026-08-25T18:27:03.531472Z","updated_at":"2026-08-30T13:33:56.636826Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb4543bd-f8e6-4aa5-a3d1-44f7be51dd41"},{"id":"8734987a-57e2-4dbd-9ce9-54ac7fc10411","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Data Scientist, Ads","slug":"senior-data-scientist-ads-a6497d93","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Location: US remote-friendly \n Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic.\n Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business.\n About the Ads Data Science Team: \n The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. \n We work on a wide range of challenging problems in the areas of ads and platform measurement, campaign and creative management, advertiser growth and retention, monetization, and the intersection of brand and community engagement. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling.\n About the Role: \n We are looking for a highly motivated and experienced Senior Data Scientist to join our growing Ads Data Science team. As a Senior Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve the adoption and performance of our advertising platform through data-driven insights. You will work closely with product managers, engineers, and other data scientists to identify opportunities, define metrics, and build solutions that drive significant impact for Reddit.\n Responsibilities: \n \n Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform\n Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform\n Collaborate with product managers and engineers to define product requirements and translate them into data science solutions\n Develop ML models \u0026 DS methods to improve anomaly detection, prediction, \u0026 pattern recognition \n Communicate findings and recommendations to stakeholders across the organization\n Stay up-to-date on the latest advancements in machine learning and data science\n Mentor and guide junior data scientists on the team\n \n Qualifications: \n \n Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research\n For M.S. holders: 5+ years of industry experience in applied science or data science roles\n For Ph.D. holders: 4+ years of industry experience in applied science or data science roles\n Platform experience and a deep understanding of the ads ecosystem\n Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design\n Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus\n Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch\n Experience with SQL and relational databases\n Excellent communication and presentation skills\n Passion for Reddit and the online advertising industry\n \n Bonus Points: \n \n Experience with online advertising and ad tech\n Experience with causal inference and A/B testing\n Contributions to open-source projects or publications in relevant conferences or journals\n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave  \n \n  \n #LI-Remote\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for ","salary_min":190800,"salary_max":267100,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["healthcare","tensorflow","pytorch","data-science"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8104403","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:32:15Z","expires_at":"2026-09-29T13:38:57.619454Z","created_at":"2026-08-25T18:28:56.611251Z","updated_at":"2026-08-30T13:38:57.752182Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8734987a-57e2-4dbd-9ce9-54ac7fc10411"},{"id":"e2cc3cda-c6a3-4f53-b1bb-9e02170e612c","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Director of Data Science and Bioinformatics","slug":"director-of-data-science-and-bioinformatics-c6f04e5e","description":"This is an exciting opportunity to lead and grow the Data Science and Bioinformatics function supporting Natera's Women's Health and Organ Health product portfolios. In this role, you will build the bioinformatics capability within the Data Science development team, establish scalable AWS cloud infrastructure, and ensure the quality and reproducibility of the genomic algorithms powering our clinical products.\n PRIMARY RESPONSIBILITIES: \n Strategy and Vision \n \n Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.\n Establish and enforce pipeline and algorithm quality standards, including review processes, validation frameworks, and documentation practices\n \n Infrastructure and Automation \n \n Own and architect scalable AWS-based data science and bioinformatics infrastructure, ensuring quality, reproducibility, and reliable deployment of Next-Generation Sequencing (NGS) algorithms.\n Implement MLOps tooling and automated validation frameworks to support reliable algorithm deployment into clinical production.\n \n Cross-Functional Collaboration \n \n Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams to implement stable, scalable pipelines and support successful productization\n \n Team Leadership \n \n Lead, mentor and hire a high-performing team of bioinformaticians and data scientists, establishing technical quality standards and clear operational ownership.\n Build technical depth within the team to support expanding product roadmaps across Women's Health and Organ Health.\n \n QUALIFICATIONS: \n \n Master of Science or Ph.D. in a quantitative technical discipline (Biostatistics, Bioinformatics, Computer Science, Physics, Applied Mathematics, or equivalent).\n Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of direct people management experience leading technical teams.\n Hands-on experience architecting AWS cloud infrastructure for data-intensive bioinformatics workloads and NGS pipeline execution.\n Demonstrated ability to identify capability gaps independently, build scalable infrastructure, and drive execution without waiting for formal structure.\n Strong communicator who builds cross-functional alignment across Research, Engineering, and Quality teams through technical clarity, direct engagement, and data-driven reasoning.\n Track record of developing bioinformatics talent and delivering computational pipelines that support commercial product development.\n \n PREFERRED QUALIFICATIONS: \n \n Experience developing software and pipelines within regulated environments (CLIA, FDA, or ISO framework).\n Experience with MLOps frameworks and pipeline tools (MLflow, Nextflow, WDL, Docker).\n Advanced knowledge of statistical inference, machine learning, and genomic data processing.\n \n Compensation \u0026 Total Rewards  \n This range reflects a good-faith estimate of the base pay we reasonably expect to offer at the time of  hire. Final compensation will vary based on experience, qualifications, and internal equity considerations. \n This position is also eligible for additional compensation and benefits through Natera’s robust Total Rewards program, including: \n \n \n Annual performance incentive bonus \n \n Long-term equity awards \n \n Comprehensive health benefits (medical, dental, vision) \n \n 401(k) with company match \n \n Generous paid time off and company holidays \n \n Additional wellness and work-life benefits \n \n \n Compensation Range \n $205,000 — $256,200 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal O","salary_min":205000,"salary_max":256200,"location":"San Carlos, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","mlops","cloud","data-science"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6142472004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:26:46Z","expires_at":"2026-09-29T13:40:54.473689Z","created_at":"2026-08-25T18:29:43.989774Z","updated_at":"2026-08-30T13:40:54.611121Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e2cc3cda-c6a3-4f53-b1bb-9e02170e612c"},{"id":"b8a8bf92-cf7f-40c2-8149-7016a7c15ff2","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Director of Data Science and Bioinformatics","slug":"director-of-data-science-and-bioinformatics-9c51ad6d","description":"This is an exciting opportunity to lead and grow the Data Science and Bioinformatics function supporting Natera's Women's Health and Organ Health product portfolios. In this role, you will build the bioinformatics capability within the Data Science development team, establish scalable AWS cloud infrastructure, and ensure the quality and reproducibility of the genomic algorithms powering our clinical products.\n PRIMARY RESPONSIBILITIES: \n Strategy and Vision \n \n Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.\n Establish and enforce pipeline and algorithm quality standards, including review processes, validation frameworks, and documentation practices\n \n Infrastructure and Automation \n \n Own and architect scalable AWS-based data science and bioinformatics infrastructure, ensuring quality, reproducibility, and reliable deployment of Next-Generation Sequencing (NGS) algorithms.\n Implement MLOps tooling and automated validation frameworks to support reliable algorithm deployment into clinical production.\n \n Cross-Functional Collaboration \n \n Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams to implement stable, scalable pipelines and support successful productization\n \n Team Leadership \n \n Lead, mentor and hire a high-performing team of bioinformaticians and data scientists, establishing technical quality standards and clear operational ownership.\n Build technical depth within the team to support expanding product roadmaps across Women's Health and Organ Health.\n \n QUALIFICATIONS: \n \n Master of Science or Ph.D. in a quantitative technical discipline (Biostatistics, Bioinformatics, Computer Science, Physics, Applied Mathematics, or equivalent).\n Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of direct people management experience leading technical teams.\n Hands-on experience architecting AWS cloud infrastructure for data-intensive bioinformatics workloads and NGS pipeline execution.\n Demonstrated ability to identify capability gaps independently, build scalable infrastructure, and drive execution without waiting for formal structure.\n Strong communicator who builds cross-functional alignment across Research, Engineering, and Quality teams through technical clarity, direct engagement, and data-driven reasoning.\n Track record of developing bioinformatics talent and delivering computational pipelines that support commercial product development.\n \n PREFERRED QUALIFICATIONS: \n \n Experience developing software and pipelines within regulated environments (CLIA, FDA, or ISO framework).\n Experience with MLOps frameworks and pipeline tools (MLflow, Nextflow, WDL, Docker).\n Advanced knowledge of statistical inference, machine learning, and genomic data processing.\n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $186,300 — $232,900 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.\n All ","salary_min":186300,"salary_max":232900,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["cloud","healthcare","mlops","data-science"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6135539004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:26:45Z","expires_at":"2026-09-29T13:40:54.361161Z","created_at":"2026-08-25T18:29:43.984963Z","updated_at":"2026-08-30T13:40:54.510623Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b8a8bf92-cf7f-40c2-8149-7016a7c15ff2"},{"id":"e416ab81-19ef-412b-8c41-f357bb53e603","company_id":"f883ba7c-f4e6-4f8e-bdcf-58c31ed3e3b4","title":"Senior Bioinformatician","slug":"senior-bioinformatician-8a07885d","description":"Vivodyne creates human data before clinical trials. \n We accelerate the successful discovery, design, and development of human therapeutics by testing on large, lab-grown human organ tissues at massive scale, driving technological advancement at the convergence of novel biology, robotics, and AI. We identify and validate new therapeutic targets and de-risk new therapeutic assets by producing clinically translatable multi-omic data from our proprietary, physiologically-realistic human organ tissues at unprecedented scale, speed, and quality. This enables us to produce more human data than all clinical trials in the U.S. combined. We’re financially backed by some of the most selective and successful venture funds, and we have already partnered with a majority of the top 10 multinational pharmaceutical companies to discover and develop better, safer drugs and dramatically reduce the burden of animal testing.\n www.vivodyne.com \n \n Role \n The Software \u0026 Data Science team at Vivodyne tackles some of the hardest problems at the intersection of human biology, large-scale data, and machine learning using uniquely rich imaging and multi-omics datasets from lab-grown human tissues.\n We are expanding our capabilities to generate high-throughput -omics data across automated human tissue models, with a primary focus on single-cell and bulk transcriptomics, secretomics, and proteomics. We're looking for a Bioinformatician who can develop new analytical methods and build robust infrastructure to support them at scale.\n You will define how we process, store, integrate, and interpret biological datasets, making foundational decisions that shape the long-term utility of our data for internal research, AI/ML model development, and external partners. This means owning both the science (what question does this analysis answer, and how defensible is the conclusion?) and the engineering (does this pipeline run reliably at scale, and can someone else extend it?).\n This is a high-impact, cross-functional role. You'll sit within the Software \u0026 Data Science organization and collaborate daily with experimental biologists, AI/ML researchers, software engineers, and client-facing teams.\n This is a full-time, onsite role in San Francisco.\n Responsibilities \n Develop and Own Analytical Methods \n \n Design, implement, and critically evaluate computational approaches for scRNA-seq, bulk RNA-seq, secretomics, and proteomics data including QC, normalization, batch correction, dimensionality reduction, clustering, differential expression, trajectory analysis, and multi-dataset integration.\n Go beyond applying standard tools: develop or adapt methods for perturbation modeling, causal inference, network reconstruction, ligand-receptor inference, and multi-omics integration as the science demands.\n Select and standardize tooling based on rigorous benchmarking, not convention. Challenge default choices when the data or use-case warrants it.\n \n Build Scalable, Production-Grade Pipelines \n \n Architect end-to-end analysis workflows in Python and/or R using modern workflow orchestration (Nextflow, Snakemake, or equivalent) that are reproducible, version-controlled, and designed to run at the scale of tens to hundreds of thousands of samples.\n Partner with software and data engineers to productionize pipelines, ensure robust data ingestion and storage, and contribute to decisions on biological data architecture (storage formats, schema design, long-term data usability).\n Write well-documented, testable code that other engineers and scientists can run, review, and extend.\n \n Drive Biological Insight and Interpretation \n \n Own dataset analysis to answer biological and client-driven questions with rigor. Ground conclusions in the limits of the data and clearly communicate what can and cannot be concluded with confidence.\n Partner with experimental biologists to frame the most relevant biological questions, design analyses, and recommend experiments and data-generation strategies to fill key gaps.\n Produce publication-quality figures, internal reports, and partner-facing deliverables that are decision-useful.\n \n Shape Data Strategy \n \n Help define benchmarking strategies for internal tissue model development.\n Proactively identify opportunities to leverage internal and public datasets to accelerate scientific progress.\n Contribute to decisions around data standards, comparability, and the integration of omics features into downstream ML models and AI workflows.\n \n Communicate Across Functions \n \n Translate complex multi-omic analyses into clear insights for biologists, AI/ML researchers, software engineers, and non-technical stakeholders.\n Tailor depth and framing to the audience while maintaining scientific rigor.\n \n Requirements and Expectations \n Technical Expertise \n \n Ph.D. in Computational Biology, Bioinformatics, Genomics, Systems Biology, Biostatistics, or a related quantitative field with deep focus on transcriptomics and/or proteomics.\n 2–5 yea","salary_min":214400,"salary_max":245000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","robotics","healthcare","data-science"],"apply_url":"https://job-boards.greenhouse.io/vivodyne/jobs/5210764007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T19:51:50Z","expires_at":"2026-09-29T13:41:03.589591Z","created_at":"2026-08-25T18:30:06.985017Z","updated_at":"2026-08-30T13:41:03.728849Z","company_name":"Vivodyne","company_slug":"vivodyne","company_logo_url":"https://www.google.com/s2/favicons?domain=vivodyne.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e416ab81-19ef-412b-8c41-f357bb53e603"},{"id":"65e8df51-d541-4df4-8f75-994aa2762e45","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Data Scientist - Experimentation Platform","slug":"senior-data-scientist-experimentation-platform-eae0fc27","description":"About the Team \n The Decision Systems Experimentation Platform team builds the platform and standards which let DoorDash, Deliveroo, and Wolt experiment successfully across every side of the marketplace, from consumers and merchants to dashers. Getting this right means faster, more reliable decisions for every team who rely on experimentation. We're still early in building a global experimentation platform across all of our brands, and this team will shape the next evolution of experimenting at DoorDash.\n About the Role \n This is a foundational role: in your first few months, you'll get close to how data scientists across DoorDash, Deliveroo, and Wolt currently experiment, and start closing the gaps between what they need and what exists today. You'll help to build the methodology, tooling, and standards other teams will rely on alongside the Experimentation Platform team currently based out of London, UK. Success looks like teams adopting what we build for their experiments. It's an exciting time to join because so much of this is still being defined whilst three brands combine, and you'll have real influence over what it becomes.\n You will report into the Data Science Manager on our Decision Systems team in our DoorDash organisation. You will be based out of our New York office and expected to occasionally travel within the US and internationally.\n You’re excited about this opportunity because you will… \n \n Design the statistical methods which encourage experimentation best practice and help teams see results faster, for use across DoorDash, Deliveroo, and Wolt.\n Work with leadership to define the vision for experimentation.\n Set the standards that experimenters across three brands and many markets rely on to trust their results.\n Partner directly with engineering to help build the platform.\n Help decide where AI belongs in the experimentation process, and where it doesn't.\n \n We’re excited about you because…\n \n You have 6+ years of experience in data science and experimentation.\n You have experience thinking about experimentation at a programme level, not only running experiments within a single team.\n You have solid technical grounding in experimentation methodology - you understand why practices like correcting for multiple comparisons or power calculations matter.\n You are comfortable working as an individual contributor while bringing stakeholder management and prioritization skills, whether or not you've managed people before.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $184,300 — $271,000 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to su","salary_min":184300,"salary_max":271000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","cloud","healthcare","data-science"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8125428","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T19:15:00Z","expires_at":"2026-09-29T13:49:20.37324Z","created_at":"2026-08-25T18:33:45.045752Z","updated_at":"2026-08-30T13:49:20.50032Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/65e8df51-d541-4df4-8f75-994aa2762e45"},{"id":"4ea25a22-d5da-4885-bd4b-07792182ccbb","company_id":"6195a3ea-00dd-46bf-a128-51a98a52d538","title":"Senior Data Scientist, AI Product Insights","slug":"senior-data-scientist-ai-product-insights-f24393d9","description":"About Mixpanel \n Mixpanel is the leading product intelligence and analytics platform, trusted by more than 29,000 companies to help understand how people use the products they build. By combining powerful analytics with AI that knows your business, Mixpanel helps teams see what’s working, diagnose what’s not, and decide what to build next. Learn more at mixpanel.com .\n About the Team\n The Proactive Insights team is a newly formed team at the center of Mixpanel's AI-first analytics vision. With a greenfield charter, we're building the intelligent layer that transforms Mixpanel from a tool you query into a partner that works for you.\n We answer the question every data-driven team asks: \"What changed, why, and what should I do about it?\" We proactively keep users informed about what matters in their data, delivering the right insights and recommendations at the right time, to the right places, both inside and outside of Mixpanel.\n Some examples of what we are building:\n \n Signals : Statistical analysis that automatically identifies which user behaviors cause downstream business outcomes — such as which actions genuinely improve 30-day retention — using causal inference to move beyond correlation\n Forecasting : Time-series modeling that projects whether a KPI (e.g. Signups) will hit its goal by end of quarter — including trend decomposition, seasonality adjustment, and confidence bands against a target.\n Simulation : Causal impact modeling that estimates how moving one metric (e.g. weekly sharing rate) by a given amount will ripple through to downstream KPIs like retention or revenue — giving teams a quantified basis for prioritization\n Cohort Detection : Automated identification of at-risk user cohorts by finding active users who resemble known churned segments across both behavioral patterns and descriptive characteristics, before they churn. Offline batch survival analysis models that estimate each user's probability of a future outcome (e.g. likelihood to churn or convert within 30 days).\n \n About the Role\n As the first Data Scientist embedded in product engineering, you'll champion integrating cutting-edge data science techniques into Mixpanel's products and serve as a methodological resource for cross-functional teams tackling problems that benefit from deeper DS expertise — such as adaptive experimentation. You won't just advise on Proactive Insights; you'll be the analytical brain driving how Signals, Forecasting, Simulation, Predictions, and Cohort Detection actually work.\n Your models are the reasoning layer behind an AI system that proactively tells customers what changed, why, and what to do next — and increasingly, the layer behind an agent that acts on their behalf. As more of this experience becomes agentic, rigorous causal grounding is what separates a trustworthy recommendation from a plausible-sounding one. You'll be the person who makes sure it's the former.\n You'll design and validate causal inference approaches that go beyond surface-level correlation, and partner on how those outputs get translated — often via LLMs — into clear, natural-language, actionable experiences for Mixpanel's customers: you own the rigor, the system owns the explanation. You'll partner closely with strong product engineers who own the implementation — your job is to make sure the methodology is rigorous, well-documented, and grounded in real outcomes. You'll also collaborate cross-functionally with teams like AI platform, analysis, and data infrastructure to scale your analytic solutions beyond what you could build alone.\n This is a high-impact, high-autonomy role on a small, fast-moving team. You'll have significant influence over the analytical direction of a new product category at Mixpanel that helps thousands of companies understand what truly drives their most important metrics.\n Responsibilities\n \n Own the end-to-end analytical design for Signals, Forecasting, Simulation, and Cohort Detection — including methodology selection, statistical validation, and iteration based on results\n Assess data quality and trust prerequisites before extending forecasting or predictive features to customers — a model is only as trustworthy as the data feeding it\n Design and apply causal inference methods to move beyond correlation and establish which user behaviors genuinely drive downstream business outcomes\n Build and own time-series forecasting models that project KPI trajectories against goals — extending our existing use of TimesFM into customer-facing forecasting features\n Build survival analysis and retention models that underpin Signals and Simulation outputs\n Develop clustering and behavioral similarity approaches for Cohort Detection that are both statistically sound and interpretable to end users\n Document methodology clearly — including assumptions, validation approaches, and expected output behavior — so engineers can implement reliably without ambiguity\n Review and validate that pr","salary_min":216000,"salary_max":254000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","llm","fine-tuning","agents","data-science"],"apply_url":"https://job-boards.greenhouse.io/mixpanel/jobs/8115028","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-10T15:48:55Z","expires_at":"2026-09-29T13:50:29.888082Z","created_at":"2026-08-25T19:52:15.800115Z","updated_at":"2026-08-30T13:50:30.093906Z","company_name":"Mixpanel","company_slug":"mixpanel","company_logo_url":"https://www.google.com/s2/favicons?domain=mixpanel.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ea25a22-d5da-4885-bd4b-07792182ccbb"},{"id":"75c90036-e512-4e00-b659-12624f47be5e","company_id":"f0134765-5cdf-4b32-b956-b7a147d9d415","title":"Senior Manager, Data Engineering","slug":"senior-manager-data-engineering-bb72e009","description":"Role Description\n \n \n We are seeking a Senior Manager, Data Engineering to lead the team responsible for Dropbox’s underlying data foundations that power our business as a whole. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions. \n  \n In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work. \n  \n The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products. \n  \n Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here . \n Responsibilities\n \n \n Data Quality \u0026 Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics. \n \n Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy. \n \n Cost \u0026 Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability. \n \n Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast. \n \n Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding tools to improve engineering productivity. \n \n Team Leadership: Lead, mentor, and grow a high-talent-density team of data engineers, fostering a culture of ownership, technical excellence, psychological safety, and continuous learning. \n \n Requirements\n \n \n 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments. \n \n 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design. \n \n Deep Technical Expertise: Proven track record building and operating large-scale batch and streaming pipelines (e.g., Spark, dbt, Airflow/orchestration) on a modern lakehouse or warehouse stack (e.g., Databricks, Snowflake, BigQuery). \n \n Reliability \u0026 Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines. \n \n Systems \u0026 Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers. \n \n Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals. \n \n Preferred Qualifications\n \n \n Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy. \n \n AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to impact and guardrails. \n \n Cost Discipline: Demonstrated success improving compute/storage unit economics without regressing reliability. \n \n Familiarity with modern data governance, privacy, and access-control practices. \n \n Experience operating in a pod or embedded model serving multiple business partners. \n \n \n Durable Skills \n AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve: \n \n Awareness: U nderstand yourself and others . \n Judgment: E valuat e information and mak e decisions in complex situations . \n Adaptability: L earn, adjust, and stay effective through change . \n Connection: C ommunicat e , collaborat e , and build trust . \n \n To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser. \n Compensation \n Canada Pay Range\n $209,100 — $282,900 CAD","salary_min":209100,"salary_max":282900,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["mlops","llm","data-engineering","data-science"],"apply_url":"https://jobs.dropbox.com/listing/8090065?gh_jid=8090065","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-04T17:49:51Z","expires_at":"2026-09-29T13:39:19.438156Z","created_at":"2026-08-25T18:29:02.708611Z","updated_at":"2026-08-30T13:39:19.573469Z","company_name":"Dropbox","company_slug":"dropbox","company_logo_url":"https://www.google.com/s2/favicons?domain=www.dropbox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/75c90036-e512-4e00-b659-12624f47be5e"},{"id":"f5676e16-77fe-4a5d-86eb-e82a6195f3c5","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Safety Data Analyst","slug":"safety-data-analyst-726dd0f0","description":"About the Company   \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.   A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.   Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.   \n Meet the Team   \n At Torc Robotics, our Safety Data Analysis team sits at the core of how we measure, understand, and continuously assess the safety performance of Torc Drive. This team blends engineering, statistics, and large-scale data analysis to transform complex, multi-modal datasets into actionable safety insights.\n What You’ll Do   \n \n Develop, implement, and refine safety performance indicators, thresholds, and targets to proactively identify, assess, and manage safety risk across on-road and simulation-based data sources.\n Apply statistically sound methods to evaluate safety performance, quantify uncertainty, and support risk-based decision-making in complex, real-world operational contexts.\n Develop automated, production-ready analysis workflows that support continuous safety monitoring, milestone gating, incident response, and safety performance evaluation.\n Ensure performance monitoring approaches appropriately reflect real-world deployment exposure, evolving operational contexts, and relevant safety requirements.\n Balance analytical rigor with timeliness to support high-consequence, time-sensitive decisions.\n Evaluate data quality, uncertainty, bias, and limitations to ensure findings are statistically defensible and useful for decision-making.\n Support data visualization, reporting, and communication strategies that help technical teams, safety stakeholders, and executives understand insights, tradeoffs, and limitations.\n Partner cross-functionally with engineering, product, verification and validation, simulation, metrics implementation, and safety teams to integrate workflows and align on data-driven decisions.\n Maintain strong documentation of analytical methods, assumptions, workflows, requirements, safety metrics, and outputs to support traceability, collaboration, and regulatory readiness.\n Ensure continuity of knowledge through peer review, cross-training, shared analytical patterns, and reproducible workflows that reduce single-point dependencies.\n \n What You’ll Need to Succeed   \n Advanced degree in Data Science, Computer Science, Math, or a closely related field:\n \n B.S. with 5+ years of experience, or\n M.S. with 3+ years of experience, or\n PhD with 1+ years of experience\n Proven experience in mechanical engineering, vehicle engineering, autonomous systems, robotics, or physics-based applications.\n Experience with vehicle and sensor systems, cloud-based data technology, data visualization, and statistical analytics.\n Strong background in applied statistics, safety analysis, risk estimation, or decision support.\n Experience working with complex, real-world datasets rather than only clean or purely academic data.\n Experience working with large-scale time-series data, vehicle data, sensor data, structured safety datasets, or other heterogeneous technical data sources.\n Proficiency in programming languages, particularly Python and SQL.\n Familiarity with agile development practices and continuous integration/deployment, including CI/CD workflows.\n Strong time management and organizational skills with the ability to prioritize tasks effectively.\n Excellent problem-solving skills with the ability to dissect complex issues and develop practical, creative solutions.\n Ability to communicate statistical and technical concepts clearly to diverse teams and stakeholders.\n Experience working in distributed teams and collaborating across different time zones.\n \n Bonus Points \n \n Background applying statistics to engineering or physics-based systems.\n Familiarity with time-series analysis, uncertainty quantification, rare-event modeling, or reliability analysis.\n Experience supporting executive, regulatory, or external stakeholder decision-making.\n Experience in simulation evaluation, scenario coverage analysis, or safety-critical system verification.\n \n Perks of Being a Full-time Torc’r   Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:        \n \n A competitive compensation package that includes a bonus component and stock options   \n \n \n 100% paid medical, dental, and vision premiums for full-time employees   \n \n \n 401K plan with a 6% employer match   \n \n \n Flexibility in schedule and generous pai","salary_min":126100,"salary_max":151300,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["payments","autonomous-vehicles","robotics","data-science"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8649136002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-31T16:20:00Z","expires_at":"2026-09-29T13:36:07.249342Z","created_at":"2026-08-25T18:27:50.490317Z","updated_at":"2026-08-30T13:36:07.386725Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f5676e16-77fe-4a5d-86eb-e82a6195f3c5"},{"id":"52277b68-b383-40c7-8131-7a3ae5014e26","company_id":"f0134765-5cdf-4b32-b956-b7a147d9d415","title":"Senior Manager, Data Engineering","slug":"senior-manager-data-engineering-c76ddd18","description":"Role Description\n \n \n We are seeking a Senior Manager, Data Engineering to lead the team responsible for Dropbox’s underlying data foundations that power our business as a whole. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions. \n  \n In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work. \n  \n The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products. \n Responsibilities\n \n \n Data Quality \u0026 Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics. \n \n Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy. \n \n Cost \u0026 Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability. \n \n Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast. \n \n Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding tools to improve engineering productivity. \n \n Team Leadership: Lead, mentor, and grow a high-talent-density team of data engineers, fostering a culture of ownership, technical excellence, psychological safety, and continuous learning. \n \n Requirements\n \n \n 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments. \n \n 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design. \n \n Deep Technical Expertise: Proven track record building and operating large-scale batch and streaming pipelines (e.g., Spark, dbt, Airflow/orchestration) on a modern lakehouse or warehouse stack (e.g., Databricks, Snowflake, BigQuery). \n \n Reliability \u0026 Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines. \n \n Systems \u0026 Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers. \n \n Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals. \n \n Preferred Qualifications\n \n \n Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy. \n \n AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to impact and guardrails. \n \n Cost Discipline: Demonstrated success improving compute/storage unit economics without regressing reliability. \n \n Familiarity with modern data governance, privacy, and access-control practices. \n \n Experience operating in a pod or embedded model serving multiple business partners. \n \n \n Durable Skills \n AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve: \n \n Awareness: U nderstand yourself and others . \n Judgment: E valuat e information and mak e decisions in complex situations . \n Adaptability: L earn, adjust, and stay effective through change . \n Connection: C ommunicat e , collaborat e , and build trust . \n \n To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser. \n Compensation \n US Zone 1 \n This role is not available in Zone 1 \n US Zone 2\n $202,700 — $274,300 USD \n US Zone 3\n $180,200 — $243,800 USD","salary_min":180200,"salary_max":243800,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["mlops","llm","data-engineering","data-science"],"apply_url":"https://jobs.dropbox.com/listing/8090062?gh_jid=8090062","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T21:04:04Z","expires_at":"2026-09-29T13:39:19.348325Z","created_at":"2026-07-30T14:09:22.265716Z","updated_at":"2026-08-30T13:39:19.478823Z","company_name":"Dropbox","company_slug":"dropbox","company_logo_url":"https://www.google.com/s2/favicons?domain=www.dropbox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/52277b68-b383-40c7-8131-7a3ae5014e26"},{"id":"52115382-539d-4a35-9403-4fc46b859257","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Data Scientist II, ML Infrastructure","slug":"data-scientist-ii-ml-infrastructure-626c42c5","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n This role focuses on advancing the science and systems behind ML measurement, feature understanding, and causal inference at scale. The work spans areas such as production feature importance platforms, observational causal estimation in Pytorch, large-scale proxy metric development, and data-driven approaches to ML infrastructure efficiency. We're looking for an enthusiastic individual contributor to perform high-impact technical work across this space. This person will drive foundational innovations, own the end-to-end design of production ML systems, establish rigorous methodological standards, and partner cross-functionally to turn successful research into durable platform capabilities that raise the ceiling for the entire ML organization.\n  \n What you’ll do: \n We are looking for an experienced and highly capable Data \u0026 Applied Scientist to help us drive step function improvements in our ML capabilities at Pinterest. \n In this role, you will:\n \n Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB \u0026 Ray while establishing reusable patterns for other teams.\n Apply and productionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities. Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.\n Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods, unlocking step-change improvements in model quality and business outcomes.\n Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks, from feature importance to content deindexing, that improve platform efficiency and speed.\n Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale across all models.\n \n  \n What we’re looking for: \n \n 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist or software engineer, with significant ML production experience.\n Strong Python skills; experience with PyTorch or equivalent deep learning frameworks; familiarity with distributed compute (Spark, Ray). Ray specifically is a strong plus.\n Enthusiasm for building tools and platforms that multiply the impact of an entire ML organization; not just solving one-off problems.\n Deep ML theory knowledge with extremely strong fundamentals that can help us reason about ML models from first principles.\n Proficiency in software development best practices including version control, code review, and reproducible ML pipelines.\n Experience with workflow management tools (Airflow, Prefect, Jenkins, or similar) for reliable ML pipeline orchestration.\n Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.\n \n  \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 3-5 times/quarter and therefore can be situated anywhere in the country.\n \n  \n #LI-NM4\n #LI-REMOTE\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including lo","salary_min":114297,"salary_max":235319,"location":"Palo Alto, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"junior","tags":["pytorch","deep-learning","data-engineering","machine-learning","infrastructure","data-science"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8071670","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-23T17:52:41Z","expires_at":"2026-09-29T13:38:52.883366Z","created_at":"2026-07-24T14:08:28.697597Z","updated_at":"2026-08-30T13:38:53.016198Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/52115382-539d-4a35-9403-4fc46b859257"},{"id":"2e84edca-4e46-4da9-aacb-3de77aafa7a9","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"Staff Data Scientist, Pricing","slug":"staff-data-scientist-pricing-587c5279","description":"Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn’t work together. So we expanded into software and started building integrated, omnichannel solutions – to help sellers sell online, manage inventory, offer buy now, pay later functionality, book appointments, engage loyal buyers, and hire and pay staff. Across it all, we’ve embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale. Today, we are a partner to sellers of all sizes – large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. We’re building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.\n The Role \n The Data Science team at Block turns insights from our unique datasets into actions that improve the customer experience every day. In this role, we're looking for a Data Scientist to own the modeling and experimentation at the core of how Square prices globally. You'll build the elasticity and willingness-to-pay models, design and run the pricing experiments, and stand up the analytical infrastructure that makes pricing measurable and controllable — shaping pricing strategy and deal-desk automation through the models and experiments you build.\n You Will \n \n Model price elasticity and willingness-to-pay across segments, geographies, and payment methods, and quantify the trade-off between margin, conversion, and merchant retention\n Design, run, and read out pricing experiments (A/B, difference-in-differences, and bandit-based dynamic tests) and translate results into recommendations that shape strategy\n Decompose merchant economics across interchange, scheme, and risk-cost layers to identify where pricing can flex and where it can't\n Build the pricing intelligence that powers Square's agentic deal tooling (DealBot) — rate recommendations, ROI and pre-approval logic, guardrail configurations, and mispricing detection — so quotes are fast, accurate, and within guardrails at scale\n Evaluate and monitor the AI systems you ship — pre-deployment testing for accuracy, boundary and edge cases, and bias in rate recommendations, and in-production monitoring for accuracy, drift, and mispricing — so agentic pricing tools stay reliable as the business changes\n Own end-to-end execution across the stack — analysis, pipeline, ETL, experimentation, and visualization\n Approach problems from first principles, using a variety of statistical and modeling techniques to understand customer behavior and price response\n Build and maintain the pricing analytics the team relies on — price realization, margin leakage, discount-waterfall, and win/loss analyses — as self-serve dashboards and curated datasets\n Measure the impact of AI-driven pricing automation with causal methods (interrupted time series, difference-in-differences) on deal velocity, quote acceptance, and margin\n Write code to process, cleanse, and combine data sources into curated ETL datasets easily used by the broader team\n Partner closely with cross-functional stakeholders across Finance, Risk, Product, and go-to-market teams, translating complex technical and AI concepts clearly for non-technical audiences\n \n You Have \n \n A bachelor degree in statistics, data science, economics, or similar STEM field with 7+ years of experience in a relevant role OR a graduate degree in statistics, data science, economics, or similar STEM field with 5+ years of experience in a relevant role\n Fluency in causal inference and experimentation, with hands-on experience modeling price elasticity or willingness-to-pay\n Prior exposure to a pricing-adjacent domain a strong plus — risk-based pricing (payments, lending, insurance), pricing science, or deal pricing analytics\n Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc)\n Experience with scripting and data analysis programming languages, such as Python or R, including using them to evaluate AI system behavior\n Gone deep with cohort and funnel analyses, with a solid understanding of statistical concepts such as selection bias, probability distributions, and conditional probabilities\n Comfort leveraging AI tools to accelerate modeling and analysis, and a working understanding of generative AI architectures — LLMs, RAG systems, and agentic AI; experience building, testing, or evaluating LLM-powered systems in produc","salary_min":239600,"salary_max":359400,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","cloud","llm","rag","generative-ai","agents","payments","data-science"],"apply_url":"http://block.xyz/careers/jobs/5364922008?gh_jid=5364922008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-22T21:46:27Z","expires_at":"2026-09-29T13:39:46.915996Z","created_at":"2026-07-24T14:09:11.95218Z","updated_at":"2026-08-30T13:39:47.057472Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2e84edca-4e46-4da9-aacb-3de77aafa7a9"}],"page":1,"per_page":20,"total":386,"total_is_exact":true,"total_pages":20}